The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology

Toshifumi Kishimoto ,Aiko Kishi,Brian Sumali,Yuki Tazawa,Kuo-Ching Liang,Takanori Fujita,Shogyoku Bun,Yasubumi Sakakibara,Yoko Eguchi,Mizuki Yotsui,Kei Funaki,Michitaka Yoshimura,Akihiro Takamiya,Hiroyoshi Toyoshiba,Masayuki Tomita,Momoko Kitazawa,Yasue Mitsukura,Masaru Mimura,Toshiro Horigome,Toshiaki Kikuchi,Tifani Warnita,Junichi Murakami,Koichi Sairyo

doi:10.1016/j.conctc.2020.100649

Abstract

IntroductionDepressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. MethodsMajor depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DiscussionThe overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. Trial RegistrationUMIN000021396, University Hospital Medical Information Network (UMIN).

Highlights

Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide
Depressive disorder is the third leading cause of years lived with disability (YLDs) that contributes to 43.1 million YLDs (95%uncertainty interval (UI) 30.5 to 58.9) [2]
The number of individuals who live with neurocognitive disorders world-wide is estimated to be 45 million (95%UI 39.7 to 50.4) [2], and these disorders contribute to 6.5 million YLDs (95%UI 4.7 to 8.6)

Summary

Introduction

Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. By collecting such data with diagnoses and/or severity information as labels, we can develop novel machine learning techniques to discover these complex patterns, which can in turn provide objective indices and predictive models for diagnosis (categorical classification) and severity assessment (continuous variable prediction), as well as for judging whether there has been an improvement/deterioration in a patient’s condition since their previous visit (categorical classification) Through these machine learning tasks, it is possible to gain additional insights into which clinical characteristics are helpful in diagnosing and evaluating severity, how to identify characteristics that parallel symptom improvement, and more. Any adverse events that occur during the study will be reported to and managed by this same review board

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